Abstract
In the early 1980's, the European chestnut tree (Castanea sativa, Mill.) assumed an important role in the Portuguese economy. Currently, the Trás-os-Montes region (Northeast of Portugal) concentrates the highest chestnuts production in Portugal, representing the major source of income in the region (€50M-€60M). The recognition of the quality of the Portuguese chestnut varieties has increasing the international demand for both industry and consumer-grade segments. As result, chestnut cultivation intensification has been witnessed, in such a way that widely disseminated monoculture practices are currently increasing environmental disaster risks. Depending on the dynamics of the location of interest, monocultures may lead to desertification and soil degradation even if it encompasses multiple causes and a whole range of consequences or impacts. In Trás-os-Montes, despite the strong increase in the cultivation area, phytosanitary problems, such as the chestnut ink disease (Phytophthora cinnamomi) and the chestnut blight (Cryphonectria parasitica), along with other threats, e.g. chestnut gall wasp (Dryocosmus kuriphilus) and nutritional deficiencies, are responsible for a significant decline of chestnut trees, with a real impact on production. The intensification of inappropriate agricultural practices also favours the onset of phytosanitary problems. Moreover, chestnut trees management and monitoring generally rely on in-field time-consuming and laborious observation campaigns. To mitigate the associated risks, it is crucial to establish an effective management and monitoring process to ensure crop cultivation sustainability, preventing at the same time risks of desertification and land degradation. Therefore, this study presents an automatic method that allows to perform chestnut clusters identification, a key-enabling task towards the achievement of important goals such as production estimation and multi-temporal crop evaluation. The proposed methodology consists in the use of Convolutional Neural Networks (CNNs) to classify and segment the chestnut fruits, considering a small dataset acquired based on digital terrestrial camera. © 2019 International Society for Photogrammetry and Remote Sensing.

Abstract
Over the last few years, the interest in the analysis of the energy consumption of Android applications has been increasing significantly. Indeed, there are a considerable number of studies which aim at analyzing the energy consumption in the Android ecosystem, such as measuring/estimating the energy consumed by an application or block of code, or even detecting energy expensive coding patterns or APIs. In this paper, we present an initial study of the impact of memoization in the energy consumption of Android applications. We compare implementations of 18 methods from different applications, with and without using memoization, and measure the energy consumption of both of them. The results show that using memoization can be a good approach for saving energy, since 13 of those methods decreased their energy consumption.

Abstract
This paper presents a minimal model of the functioning of program verification and property checking tools based on (i) the encoding of loops as non-iterating programs, either conservatively, making use of invariants and assume/assert commands, or in a bounded way; and (ii) the use of an intermediate single-assignment (SA) form. The model captures the basic workflow of tools like Boogie, Why3, or CBMC, building on a clear distinction between operational and axiomatic semantics. This allows us to consider separately the soundness of program annotation, loop encoding, translation into SA form, and VC generation, as well as appropriate notions of completeness for each of these processes. To the best of our knowledge, this is the first formalization of a bounded model checking of software technique, including soundness and completeness proofs using Hoare logic; we also give the first completeness proof of a deductive verification technique based on a conservative encoding of invariant-annotated loops with assume/assert in SA form, as well as the first soundness proof based on a program logic. © 2019 IEEE.

Abstract
This paper presents the GreenSource infrastructure: a large body of open source code, executable Android applications, and curated dataset containing energy code metrics. The dataset contains energy metrics obtained by both static analysing the applications' source code and by executing them with available test inputs. To automate the execution of the applications we developed the AnaDroid tool which instruments its code, compiles and executes it with test inputs in any Android device, while collecting energy metrics. GreenSource includes all Android applications included in the MUSE Java source code repository, while AnaDroid implements all Android's energy greedy features described in the literature, GreenSource aims at characterizing energy consumption in the Android ecosystem, providing both Android developers and researchers a setting to reason about energy efficient Android software development. © 2019 IEEE.

Abstract
In recent years unmanned aerial vehicles (UAVs) have been used in several applications and research studies related to environmental monitoring. The works performed have demonstrated the suitability of UAVs to be employed in different scenarios, taking advantage of its capacity to acquire high-resolution data from different sensing payloads, in a timely and flexible manner. In forestry ecosystems, UAVs can be used with accuracies comparable with traditional methods to retrieve different forest properties, to monitor forest disturbances and to support disaster monitoring in fire and post-fire scenarios. In this study an area recently affected by a wildfire was surveyed using two UAVs to acquire multi-spectral data and RGB imagery at different resolutions. By analysing the surveyed area, it was possible to detect trees, that were able to survive to the fire. By comparing the ground-truth data and the measurements estimated from the UAV-imagery, it was found a positive correlation between burned height and a high correlation for tree height. The mean NDVI value was extracted used to create a three classes map. Higher NDVI values were mostly located in trees that survived that were not/barely affected by the fire. The results achieved by this study reiterate the effectiveness of UAVs to be used as a timely, efficient and cost-effective data acquisition tool, helping for forestry management planning and for monitoring forest rehabilitation in post-fire scenarios. © 2019 International Society for Photogrammetry and Remote Sensing.

Abstract
Virtual Reality (VR) has been recently gaining interest from researchers and companies, contributing to the development of the associated technologies that aim to transport its users to a virtual environment by the stimulation of their senses. Technologies such as Head-Mounted Displays (HMD), capable of presenting 360 degrees video in 3D, are becoming affordable and, consequently, more common among the average consumer, potentiating the creation of a market for VR experiences. The purpose of this study is to measure the influence of (a) video format (2D/monoscopic vs 3D/stereoscopic), (b) sound format (2D/stereo vs 3D/spatialized), and (c) gender on users' sense of presence and cybersickness, while experiencing a VR application using an HMD. Presence and cybersickness were measured using questionnaires as subjective measures. Portuguese versions of the Igroup Presence Questionnaire for presence and the Simulator Sickness Questionnaire for cybersickness were used. Results revealed no statistically significant differences between (a) VIDEO and (b) SOUND variables on both senses of presence and cybersickness. When paired with (a) VIDEO, the independent variable (c) Gender showed significant differences on almost all subscales of presence. Results suggest that the widely acknowledged differences in spatial ability between genders were a major factor contributing to this outcome.